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Handwritten dynamics assessment through convolutional neural networks

  • Background and objective Parkinson’s disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. Methods In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual’s assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. Results The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. Conclusions The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson’s disease identification, as well as it can outperform handcrafted features.

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Metadaten
Author:Clayton R. PereiraORCiD, Danilo R. PereiraORCiD, Gustavo H. Rosa, Victor Hugo C. Albuquerque, Silke A. T. WeberORCiD, Christian Hook, João Paulo PapaORCiD
DOI:https://doi.org/10.1016/j.artmed.2018.04.001
Parent Title (English):Artificial Intelligence in Medicine
Subtitle (German):An application to Parkinson’s disease identification
Publisher:Elsevier
Document Type:Article
Language:English
Year of first Publication:2018
Release Date:2022/03/29
Tag:Convolutional neural networks; Handwritten dynamics; Parkinson’s disease
Volume:37
Issue:May
First Page:67
Last Page:77
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG